[1] [1] 董小龙， 胡修棉， 赖文. 雅鲁藏布江砂粒显微图像数据集［J］. 中国科学数据， 2020， 5（3）： 34-43. doi: 10.11922/csdata.2020.0051.zhDONGX L， HUX M， LAIW. A photomicrograph dataset of sand grains from the Yarlung Tsangpo， Tibet［J］. China Scientific Data， 2020， 5（3）： 34-43.（in Chinese）. doi: 10.11922/csdata.2020.0051.zh

[2] RAMESH K K D, KUMAR G, SWAPNA K et al. A review of medical image segmentation algorithms[J]. EAI Endorsed Transactions on Pervasive Health and Technology, 169184(2018).

[3] YARMOHAMMADI S, WOOD D A, KADKHODAIE A. Reservoir microfacies analysis exploiting microscopic image processing and classification algorithms applied to carbonate and sandstone reservoirs[J]. Marine and Petroleum Geology, 121, 104609(2020).

[4] SAFARI H, BALCOM B J, AFROUGH A. Characterization of pore and grain size distributions in porous geological samples - an image processing workflow[J]. Computers and Geosciences, 156, 104895(2021).

[5] CHEN Z H, LIU X J, YANG J J et al. Deep learning-based method for SEM image segmentation in mineral characterization， an example from Duvernay Shale samples in Western Canada Sedimentary Basin[J]. Computers and Geosciences, 138, 104450(2020).

[6] PARE S, KUMAR A, SINGH G K et al. Image segmentation using multilevel thresholding： a research review[J]. Iranian Journal of Science and Technology， Transactions of Electrical Engineering, 44, 1-29(2020).

[7] LUO G, PAN S K, ZHANG Y L et al. Research on establishing numerical model of geo material based on CT image analysis[J]. EURASIP Journal on Image and Video Processing, 2019, 36(2019).

[8] WANG C C, ZHAO H, SHENG G L et al. Multi-scale and multi-region pore structure analysis on sandy conglomerate whole core with digital rock model[J]. Journal of Energy Resources Technology, 145(2023).

[9] WANG M, WANG W, FENG S et al. Adaptive multi-class segmentation model of aggregate image based on improved sparrow search algorithm[J]. KSII Transactions on Internet and Information Systems, 17, 391-411(2023).

[10] [10] 杨蕴， 李玉， 赵泉华. 高分辨率全色遥感图像多级阈值分割［J］. 光学 精密工程， 2020， 28（10）： 2370-2383. doi: 10.37188/OPE.20202810.2370YANGY， LIY， ZHAOQ H. Multi-level threshold segmentation of high-resolution panchromatic remote sensing imagery［J］. Opt. Precision Eng.， 2020， 28（10）： 2370-2383.（in Chinese）. doi: 10.37188/OPE.20202810.2370

[11] OUCHICHA C, AMMOR O, MEKNASSI M. A new approach based on exponential entropy with modified kernel fuzzy c-means clustering for MRI brain segmentation[J]. Evolutionary Intelligence, 16, 651-665(2023).

[12] SINGH GILL H, SINGH KHEHRA B, SINGH A et al. Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values[J]. Egyptian Informatics Journal, 20, 11-25(2019).

[13] ZHENG Z, ZHA B T, XUCHEN Y S et al. Adaptive edge detection algorithm based on grey entropy theory and textural features[J]. IEEE Access, 7, 92943-92954(2943).

[14] WANG Y, ZHANG G B, ZHANG X F. Multilevel image thresholding using tsallis entropy and cooperative pigeon-inspired optimization bionic algorithm[J]. Journal of Bionic Engineering, 16, 954-964(2019).

[15] ZHANG H, FRITTS J E, GOLDMAN S A. An entropy-based objective evaluation method for image segmentation[C], 38-49(2003).

[16] [16] 张坤华， 谭志恒， 李斌. 结合粒子群优化和综合评价的脉冲耦合神经网络图像自动分割［J］. 光学 精密工程， 2018， 26（4）： 962-970. doi: 10.3788/ope.20182604.0962ZHANGK H， TANZ H， LIB. Automated image segmentation based on pulse coupled neural network with partide swarm optimization and comprehensive evaluation［J］. Opt. Precision Eng.， 2018， 26（4）： 962-970.（in Chinese）. doi: 10.3788/ope.20182604.0962

[17] DHAL K G et al. Nature-inspired optimization algorithms and their application in multi-thresholding image segmentation[J]. Archives of Computational Methods in Engineering, 27, 855-888(2020).

[18] MIRJALILI S, GANDOMI A H, MIRJALILI S Z et al. Salp swarm algorithm： a bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 114, 163-191(2017).

[19] BAIRWA A K, JOSHI S, SINGH D. Dingo optimizer： a nature-inspired metaheuristic approach for engineering problems[J]. Mathematical Problems in Engineering, 2021, 1-2(2021).

[20] BRAIK M, HAMMOURI A, ATWAN J et al. White Shark Optimizer： a novel bio-inspired meta-heuristic algorithm for global optimization problems[J]. Knowledge-Based Systems, 243, 108457(2022).

[21] XIE L, HAN T, ZHOU H et al. Tuna swarm optimization： a novel swarm-based metaheuristic algorithm for global optimization[J]. Computational Intelligence and Neuroscience, 2021, 9210050(2021).

[22] MIRJALILI S. SCA： a Sine Cosine Algorithm for solving optimization problems[J]. Knowledge-Based Systems, 96, 120-133(2016).

[23] BRAIK M, RYALAT M H, AL-ZOUBI H. A novel meta-heuristic algorithm for solving numerical optimization problems： Ali Baba and the forty thieves[J]. Neural Computing and Applications, 34, 409-455(2022).

[24] XIAO R G, LIU G Q, YI D R et al. Study on prediction model of liquid holdup based on back propagation neural network optimized by tuna swarm algorithm[J]. Energy Sources， Part A： Recovery， Utilization， and Environmental Effects, 45, 8623-8641(2023).

[25] QIN Z B, XU H, JIN Y et al. Multi-strategy improved tuna swarm optimization algorithm for feature selection of network intrusion detection[C], 12717, 681-686(2023).

[26] NANDA B, MUNI B P, JENA R K. Enhancing power quality in microgrids with hybrid tuna-glowworm swarm optimization strategy for renewable energy sources[J]. Energy Technology, 12, 2300067(2024).

[27] ALSATTAR H A, ZAIDAN A A, ZAIDAN B B. Novel meta-heuristic bald eagle search optimisation algorithm[J]. Artificial Intelligence Review, 53, 2237-2264(2020).

[28] ABDOLLAHZADEH B, GHAREHCHOPOGH F S, MIRJALILI S. African vultures optimization algorithm： a new nature-inspired metaheuristic algorithm for global optimization problems[J]. Computers & Industrial Engineering, 158, 107408(2021).

[29] GOTTHANS T, SPROTT J C, PETRZELA J. Simple chaotic flow with circle and square equilibrium[J]. International Journal of Bifurcation and Chaos, 26, 1650137(2016).

[30] ZHANG M J, LONG D Y, QIN T et al. A chaotic hybrid butterfly optimization algorithm with particle swarm optimization for high-dimensional optimization problems[J]. Symmetry, 12, 1800(2020).

[31] TUERXUN W, XU C, GUO H Y et al. An ultra-short-term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition[J]. Energy Science & Engineering, 10, 3001-3022(2022).

[32] KUMAR C, MAGDALIN MARY D. A novel chaotic-driven Tuna Swarm Optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules[J]. Optik, 264, 169379(2022).

[33] HOUSSEIN E H, MOHAMED G M, IBRAHIM I A et al. An efficient multilevel image thresholding method based on improved heap-based optimizer[J]. Scientific Reports, 13, 9094(2023).